% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DIscBIO-generic-Normalizedata.R
\name{Normalizedata}
\alias{Normalizedata}
\alias{Normalizedata,DISCBIO-method}
\title{Normalizing and filtering}
\usage{
Normalizedata(
  object,
  mintotal = 1000,
  minexpr = 0,
  minnumber = 0,
  maxexpr = Inf,
  downsample = FALSE,
  dsn = 1,
  rseed = NULL
)

\S4method{Normalizedata}{DISCBIO}(
  object,
  mintotal = 1000,
  minexpr = 0,
  minnumber = 0,
  maxexpr = Inf,
  downsample = FALSE,
  dsn = 1,
  rseed = NULL
)
}
\arguments{
\item{object}{\code{DISCBIO} class object.}

\item{mintotal}{minimum total transcript number required. Cells with less
than \code{mintotal} transcripts are filtered out. Default is 1000.}

\item{minexpr}{minimum required transcript count of a gene in at least
\code{minnumber} cells. All other genes are filtered out. Default is 0.}

\item{minnumber}{minimum number of cells that are expressing each gene at
minexpr transcripts. Default is 0.}

\item{maxexpr}{maximum allowed transcript count of a gene in at least a
single cell after normalization or downsampling. All other genes are
filtered out. Default is Inf.}

\item{downsample}{A logical vector. Default is FALSE. If downsample is set to
TRUE, then transcript counts are downsampled to mintotal transcripts per
cell, instead of the normalization. Downsampled versions of the transcript
count data are averaged across dsn samples}

\item{dsn}{A numeric value of the number of samples to be used to average the
downsampled versions of the transcript count data. Default is 1 which means
that sampling noise should be comparable across cells. For high numbers of
dsn the data will become similar to the median normalization.}

\item{rseed}{Random integer to enforce reproducible clustering.
results}
}
\value{
The DISCBIO-class object input with the ndata and fdata slots filled.
}
\description{
This function allows filtering of genes and cells to be used in
  the downstream analysis.
}
\examples{
sc <- DISCBIO(valuesG1msTest) # changes signature of data

# In this case this function is used to normalize the reads
sc_normal <- Normalizedata(
  sc,
  mintotal = 1000, minexpr = 0, minnumber = 0, maxexpr = Inf,
  downsample = FALSE, dsn = 1, rseed = 17000
)
summary(sc_normal@fdata)

}